Documentdetail
ID kaart
oai:arXiv.org:2411.01652
Onderwerp
Computer Science - Computer Vision... Computer Science - Artificial Inte...Auteur
Ahlawat, Vaneeta Sharma, Rohit UrushCategorie
Computer Science
Jaar
2024
vermelding datum
06-11-2024
Trefwoorden
computer vceBeschrijving
In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system.
The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomously classify GI anomalies from VCE images.
This paper presents CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state.
;Comment: 11 pages, 7 figuers
Ahlawat, Vaneeta,Sharma, Rohit,Urush, 2024, Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
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